def main():
args = parse_args()
assert args.out or args.show or args.json_out, (
"Please specify at least one operation (save or show the results) "
'with the argument "--out" or "--show" or "--json_out"'
)
if args.out is not None and not args.out.endswith((".pkl", ".pickle")):
raise ValueError("The output file must be a pkl file.")
if args.json_out is not None and args.json_out.endswith(".json"):
args.json_out = args.json_out[:-5]
cfg = torchie.Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get("cudnn_benchmark", False):
torch.backends.cudnn.benchmark = True
# cfg.model.pretrained = None
cfg.data.test.test_mode = True
# cfg.data.val.test_mode = True
# init distributed env first, since logger depends on the dist info.
if args.launcher == "none":
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# build the dataloader
# TODO: support multiple images per gpu (only minor changes are needed)
dataset = build_dataset(cfg.data.test)
# dataset = build_dataset(cfg.data.val)
data_loader = build_dataloader(
dataset,
batch_size=cfg.data.samples_per_gpu,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False,
)
# build the model and load checkpoint
model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
checkpoint = load_checkpoint(model, args.checkpoint, map_location="cpu")
# old versions did not save class info in checkpoints, this walkaround is
# for backward compatibility
if "CLASSES" in checkpoint["meta"]:
model.CLASSES = checkpoint["meta"]["CLASSES"]
else:
model.CLASSES = dataset.CLASSES
model = MegDataParallel(model, device_ids=[0])
result_dict, detections = test(
data_loader, model, save_dir=None, distributed=distributed
)
for k, v in result_dict["results"].items():
print(f"Evaluation {k}: {v}")
rank, _ = get_dist_info()
if args.out and rank == 0:
print("\nwriting results to {}".format(args.out))
torchie.dump(detections, args.out)
if args.txt_result:
res_dir = os.path.join(os.getcwd(), "predictions")
for dt in detections:
with open(
os.path.join(res_dir, "%06d.txt" % int(dt["metadata"]["token"])), "w"
) as fout:
lines = kitti.annos_to_kitti_label(dt)
for line in lines:
fout.write(line + "\n")
ap_result_str, ap_dict = kitti_evaluate(
"/data/Datasets/KITTI/Kitti/object/training/label_2",
res_dir,
label_split_file="/data/Datasets/KITTI/Kitti/ImageSets/val.txt",
current_class=0,
)
print(ap_result_str)
def main():
args = parse_args()
print(args)
assert args.out or args.show or args.json_out, (
'Please specify at least one operation (save or show the results) with the argument "--out" or "--show" or "--json_out"'
)
if args.out is not None and not args.out.endswith((".pkl", ".pickle")):
raise ValueError("The output file must be a pkl file.")
if args.json_out is not None and args.json_out.endswith(".json"):
args.json_out = args.json_out[:-5]
cfg = torchie.Config.fromfile(args.config)
if cfg.get("cudnn_benchmark", False): # False
torch.backends.cudnn.benchmark = True
# cfg.model.pretrained = None
# cfg.data.test.test_mode = True
cfg.data.val.test_mode = True
# init distributed env first, since logger depends on the dist info.
if args.launcher == "none":
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# build the dataloader, TODO: support multiple images per gpu (only minor changes are needed)
dataset = build_dataset(cfg.data.val)
batch_size = cfg.data.samples_per_gpu
num_workers = cfg.data.workers_per_gpu
data_loader = DataLoader(
dataset,
batch_size=batch_size,
sampler=None,
num_workers=num_workers,
collate_fn=collate_kitti,
shuffle=False,
)
# build the model and load checkpoint
model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
checkpoint_path = os.path.join(cfg.work_dir, args.checkpoint)
checkpoint = load_checkpoint(model, checkpoint_path, map_location="cpu")
# old versions did not save class info in checkpoints, this walkaround is for backward compatibility
if "CLASSES" in checkpoint["meta"]:
model.CLASSES = checkpoint["meta"]["CLASSES"]
else:
model.CLASSES = dataset.CLASSES
model = MegDataParallel(model, device_ids=[0])
if args.eval_id is None:
result_dict, detections = test(data_loader,
model,
save_dir=None,
distributed=distributed)
# for k, v in result_dict["results"].items():
# print(f"Evaluation {k}: {v}")
for k, v in result_dict["results"].items():
print(f"Evaluation {k}: {v}")
# f.write(f"\nEvaluation {k}: {v}\n")
for k, v in result_dict["results_2"].items():
print(f"Evaluation {k}: {v}")
# f.write(f"\nEvaluation {k}: {v}\n")
# save mAP results to out.pkl file.
# rank, _ = get_dist_info()
# if args.out and rank == 0:
# print("\nwriting results to {}".format(args.out))
# torchie.dump(detections, os.path.join(cfg.work_dir, args.out))
# if args.txt_result: # True
# res_dir = os.path.join(cfg.work_dir, "predictions")
# os.makedirs(res_dir, exist_ok=True)
# for dt in detections:
# with open(os.path.join(res_dir, "%06d.txt" % int(dt["metadata"]["token"])), "w") as fout:
# lines = kitti.annos_to_kitti_label(dt)
# for line in lines:
# fout.write(line + "\n")
# gt_labels_dir = data_root + "/KITTI/object/training/label_2"
# label_split_file = data_root + "/KITTI/ImageSets/val.txt"
# # todo: this evaluation is different from previous one
# ap_result_str, ap_dict = kitti_evaluate(gt_labels_dir, res_dir, label_split_file=label_split_file, current_class=0,)
# print(ap_result_str)
else:
assert type(args.eval_id) is list
test_v2(data_loader,
model,
distributed=distributed,
eval_id=args.eval_id,
vis_id=args.vis_id)